Impact of Temporal and Spatial Application Modeling on Event-Triggered Wireless Sensor Network Evaluation

2015 ◽  
Author(s):  
Lisane Brisolara ◽  
Paulo R. Ferreira ◽  
Leandro Soares Indrusiak
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Temporal And Spatial ◽  
Network Evaluation ◽  
Event Triggered

Self-powered event-triggered wireless sensor network for monitoring sabotage activities

2016 ◽  
Author(s):  
Chuan Dong ◽  
Suiqiong Li ◽  
Mengyang Li ◽  
Qisheng He ◽  
Dacheng Xu ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Self Powered ◽  
Event Triggered

Self‐powered wireless sensor network using event‐triggered energy harvesters for monitoring and identifying intrusion activities

2019 ◽  
Vol 12 (8) ◽  
pp. 2079-2085 ◽  
Author(s):  
Chuan Dong ◽  
Suiqiong Li ◽  
Ruofeng Han ◽  
Qisheng He ◽  
Xinxin Li ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Energy Harvesters ◽  
Self Powered ◽  
Event Triggered

scholarly journals A Missing Sensor Data Estimation Algorithm Based on Temporal and Spatial Correlation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhipeng Gao ◽  
Weijing Cheng ◽  
Xuesong Qiu ◽  
Luoming Meng
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Spatial Correlation ◽  
Two Dimensions ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Data Loss ◽  
Temporal And Spatial ◽  
Data Estimation

In wireless sensor network, data loss is inevitable due to its inherent characteristics. This phenomenon is even serious in some situation which brings a big challenge to the applications of sensor data. However, the traditional data estimation methods can not be directly used in wireless sensor network and existing estimation algorithms fail to provide a satisfactory accuracy or have high complexity. To address this problem,Temporal and Spatial Correlation Algorithm(TSCA) is proposed to estimate missing data as accurately as possible in this paper. Firstly, it saves all the data sensed at the same time as a time series, and the most relevant series are selected as the analysis sample, which improves efficiency and accuracy of the algorithm significantly. Secondly, it estimates missing values from temporal and spatial dimensions. Different weights are assigned to these two dimensions. Thirdly, there are two strategies to deal with severe data loss, which improves the applicability of the algorithm. Simulation results on different sensor datasets verify that the proposed approach outperforms existing solutions in terms of estimation accuracy.

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